SELF-ADAPTIVE ALGORITHMS FOR SOLVING CONVEX BILEVEL OPTIMIZATION PROBLEMS

被引：0
|
作者：
Zhao, Bowen [1 ]
Duan, Peichao [1 ]
机构：
[1] Civil Aviat Univ China, Coll Sci, Tianjin 300300, Peoples R China
来源：
JOURNAL OF NONLINEAR FUNCTIONAL ANALYSIS | 2023年 / 2023卷 / 01期
关键词：
Convex bilevel optimization; Inertial acceleration; Moreau-Yosida approximate; Self-adaptive algorithm; Split proximal algorithm; PSEUDOMONOTONE VARIATIONAL-INEQUALITIES; SPLIT FEASIBILITY PROBLEMS;
D O I：
10.23952/jnfa.2023.16
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we present a self-adaptive algorithm and an inertial version for solving convex bilevel optimization problems. We establish the strong convergence of our proposed algorithms. The step-sizes in our algorithms for the inner level optimization problem are selected without prior knowledge of operator norms. A numerical experiment is included to illustrate the performances of our algorithms and some comparisons are present with related algorithms.
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页数：14
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